Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection

Algorithm 2

The proposed algorithm.

	Require: fundus images (X,Y); where Y = {y/y ϵ {normal, mild, moderate, severe, PDR}}
	Output: the trained model that classifies the fundus images x ϵ X
(1)	Perform preprocessing:
(i)	Resize the image to dimension 786 × 512
(ii)	Perform augmentation: randomly crop five patches, of size 512 × 512, of each image and perform flip flop and 90 degree rotation
(iii)	Mean normalize the each image
	Import a set of pretrained models  = {Dense121, ResNet50, Inception-V3}
	Replace the last fully connected layer of each model by a layer of (5 × 1) dimension
	foreach ∀h ϵ  do
	α = 0.0001
	for epochs = 1 to 50 do
	foreach mini batch(Xi,Yi) ϵ (Xtrain, Ytrain) do
	Update the parameters of the model h(.) using Eq.2
	if the validation error is not improving for five epochs then
	stop training
	end
	end
	end
	end
	foreach x ϵ Xtest do
	Ensemble the output of all models, h ϵ, using equation (3)
	End